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Date: Q2 2021 Repository: [Link] Notebook: [Link] PDF Slides:[Link]

Description:

The dataset contains ~100k records of a company's sales, customer, operational and product data. The project involved: data loading, data cleaning, preprocessing, filling missing values, exploratory data analysis, measuring statistical factors, hypothesis testing.

Results: Data-based business recommendations for the company.

Skills: Data cleaning | Data analysis | Descriptive statistics | Data visualization

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Date: Q2 2022 Duration: 35 hours Repository:[Link] Notebooks: [Data aggregation] | [Data analysis]

Description:

A course aimed at learning to use Pandas (Python library) for data aggregation and data analysis.
There were two capstone projects, one for each core skill (data aggregation & exploratory data analysis).

Skills: Data cleaning | Data analysis | Descriptive statistics | Data visualization | Statistics | Machine learning | Time series

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Date: Q2 2022 Repository: [Link]

Description:

A Google sheets formula to speed up a recurring process. The formula allows users to input more than one email address in the receiver field of a Google Form, splits out the data and duplicates the rows in the back end (1 duplication per email entered into the receiver field). This repository documents this project.

Skills: Spreadsheet formulas | Google suite

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Date: Q2 2022 Repository: [Link]

Description:

A Google Apps Script to speed up a repetitive, manual process and reduce input errors. The script creates two dropdown menus in a Google Sheet, where the second dropdown menu is dependant on the input of the first dropdown menu. This repository documents this project.

Skills: Google Apps Script | Google suite

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• Automate tasks on their computer by writing simple Python programs.
• Write programs that can do text pattern recognition with "regular expressions".
• Programmatically generate and update Excel spreadsheets.
• Parse PDFs and Word documents.
• Crawl web sites and pull information from online sources.
• Write programs that send out email notifications.
• Use Python's debugging tools to quickly figure out bugs in your code.
• Programmatically control the mouse and keyboard to click and type for you.

• Pandas for Data Manipulation
• NumPy and Python for Numerical Processing
• Pandas for Data Visualization
• How to Work with Time Series Data with Pandas
• Use Statsmodels to Analyze Time Series Data
• Evaluate a model's efficiency by comparing training and test data
• Use Facebook's Prophet Library for forecasting
• Understand advanced ARIMA models for Forecasting

• Understand the fundamentals of statistics
• Learn how to work with different types of data
• How to plot different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Calculate correlation and covariance
• Distinguish and work with different types of distributions
• Estimate confidence intervals
• Perform hypothesis testing
• Make data driven decisions
• Understand the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables
• Understand the concepts needed for data science even with Python and R

• Bring your data handling & data analysis skills to an outstanding level.
• Master a complete machine learning project A-Z with Pandas, Scikit-Learn, and Seaborn
• Practice and master your Pandas skills with quizzes, 150+ exercises, and comprehensive projects
• Learn and master the most important Pandas workflows for finance
• Learn the basics of Pandas and Numpy coding
• Learn and practice all relevant Pandas methods and workflows with real-world datasets
• Import, clean, and merge messy data and prepare data for machine learning
• Analyze, visualize, and understand your data with Pandas, Matplotlib, and Seaborn
• Import financial/stock data from web sources and analyze them with Pandas
• Learn and master important statistical concepts with scipy

• Introduction to SQL
• Joining data in SQL
• Intermediate SQL
• PostgresSQL summary stats and window functions
• Functions for Manipulating Data in PostgreSQL

• SQL statement fundamentals (Select, Count, Where, Order by, Limit, In, (I)like)
• Group by statements (Group by, Having)
• Joins (As statement, Inner joins, Full outer joins, Left outer joins, Right joins, Union)
• Advanced SQL commands (Timestamps, extract, mathematical functions, string functions, subquery, self-join)
• Creating databases and tables (data types, primary & foreign keys, constraints, create table, insert, update, delete, alter table, drop table, check constraint)
• Conditional expressions and procedures (case, coalesce, cast, nullif, views, import, export)

The course offers participants an introduction to the foundation skills and knowledge of a middle manager and leader. The learning activities begin the process of preparing the learner for the Chartered Management Institute (CMI) qualifications in Management and Leadership at Level 5. It introduces them as experienced practitioners to the underpinning theory of management and leadership. The course was prepared by The Open University Business School (AMBA, EQUIS, AACSB triple-accredited)